MVar (version 2.2.7)

MDS: Multidimensional Scaling (MDS).

Description

Performs Multidimensional Scaling (MDS) on a data set.

Usage

MDS(data, distance = "euclidean", title = NA, xlabel = NA,  
    ylabel = NA, posleg = 2, boxleg = TRUE, axes = TRUE, 
    size = 1.1, grid = TRUE, color = TRUE, linlab = NA, 
    class = NA, classcolor = NA, savptc = FALSE, width = 3236, 
    height = 2000, res = 300)

Value

Multidimensional Scaling.

mtxD

Matrix of the distances.

Arguments

data

Data to be analyzed.

distance

Metric of the distance: "euclidean" (default), "maximum", "manhattan", "canberra", "binary" or "minkowski".

title

Titles of the graphics, if not set, assumes the default text.

xlabel

Names the X axis, if not set, assumes the default text.

ylabel

Names the Y axis, if not set, assumes the default text.

posleg

0 with no caption,
1 for caption in the left upper corner,
2 for caption in the right upper corner (default),
3 for caption in the right lower corner,
4 for caption in the left lower corner.

boxleg

Puts the frame in the caption (default = TRUE).

axes

Plot the X and Y axes (default = TRUE).

size

Size of the points in the graphs.

grid

Put grid on graphs (default = TRUE).

color

Colored graphics (default = TRUE).

linlab

Vector with the labels for the observations.

class

Vector with names of data classes.

classcolor

Vector with the colors of the classes.

savptc

Saves graphics images to files (default = FALSE).

width

Graphics images width when savptc = TRUE (defaul = 3236).

height

Graphics images height when savptc = TRUE (default = 2000).

res

Nominal resolution in ppi of the graphics images when savptc = TRUE (default = 300).

Author

Paulo Cesar Ossani

Marcelo Angelo Cirillo

References

Mingoti, S. A. Analise de dados atraves de metodos de estatistica multivariada: uma abordagem aplicada. Belo Horizonte: UFMG, 2005. 297 p.

Rencher, A. C. Methods of multivariate analysis. 2th. ed. New York: J.Wiley, 2002. 708 p.

Examples

data(iris) # data set

data <- iris[,1:4]

cls <- iris[,5] # data class

md <- MDS(data = data, distance = "euclidean", title = NA, xlabel = NA,  
          ylabel = NA, posleg = 2, boxleg = TRUE, axes = TRUE, color = TRUE,
          linlab = NA, class = cls, classcolor = c("goldenrod3","gray53","red"),
          savptc = FALSE, width = 3236, height = 2000, res = 300)
          
print("Matrix of the distances:"); md$mtxD